October is Pink Ribbon Month: an annual campaign to increase awareness about breast cancer and get more women screened in order to catch the disease in its early stages, which will boost survival rates. And as part of the campaign, buildings are lit up in pink and cancer survivors and doctors hold lectures nationwide to get the message out. But experts in Japan remain divided -- and undecided -- on one issue surrounding breast cancer screenings and that is whether to tell people who undergo the tests if they have dense breast tissue. Normal breast tissue is composed of milk glands, milk ducts, fatty tissue and supportive tissue that is dense breast tissue. For those with dense breasts, they have more dense tissue than fatty tissue.
Radnet shares our mission to improve the early detection and diagnostics of breast disease, and ultimately reduce suffering by materially reducing the incidence of late-stage cancers. We are working with RadNet's team to create solutions consistent with our shared visions of customer-focused medicine and attention to the patient experience. The objectives of our collaboration with RadNet are to improve mammography compliance by women across the country, enhance their imaging center experience and drive more positive outcomes through early detection."
At RSNA 2019, attendees will have the opportunity to learn how artificial intelligence (AI) is transforming women's imaging, such as enhancing breast imaging, improving radiologists' workflow, and reducing recall rates with digital mammography. This year's conference will also feature presentations on shear-wave elastography, contrast-enhanced spectral mammography, and more. Presentations will highlight how AI can improve or enhance breast imaging, covering topics such as the use of deep learning to reduce digital breast tomosynthesis (DBT) reading time and even further improve its cancer detection capability; the benefits of allowing AI algorithms to sift through mammograms and eliminate low-malignancy exams, thus improving radiologists' workflow; using AI as a tool to reduce the recall rate on digital mammography; and machine learning-based evaluation of DBT screening through the creation of customized synthesized 2D images. In fact, the RSNA plans to kick off the week with a Deep Learning Classroom session that's part of its AI Showcase, and the session will be repeated throughout the meeting. Yet even with all this interest in AI, RSNA 2019 will offer attendees a chance to explore a variety of other women's imaging matters as well.
A new study suggests that an artificial intelligence system may be able to perform tasks as accurately as a highly trained radiologist. The paper published in the Journal of the National Cancer Institute outlines how an AI system can accurately detect evaluate digital mammography in breast cancer screenings. Breast cancer screenings are an important tool in the early detection of breast cancer and the reduction of breast cancer-related mortality. Screenings currently are very labor intensive due to the high volume of women needing scans. In some parts of the world, including the U.S. there is a scarcity in the number of highly trained breast screening radiologists which has led to the development of AI systems that can do some of the tasks related to evaluating mammograms.
With the backing of the White House, Seattle cancer researchers are enlisting artificial intelligence experts across the globe to catch the early signs of breast cancer. The Digital Mammography DREAM Challenge – a partnership of Seattle Cancer Care Alliance, Group Health Research Institute, Fred Hutchinson Cancer Research Center, Sage Bionetworks and others – was announced at Vice President Joe Biden's Cancer Moonshot Summit that in June brought together health care providers, research institutions and technology experts for a national day of action. For the challenge, more than 300 tech experts at machine and deep learning labs around the world will get data on 86,000 breast cancer patients, including more than 640,000 digital mammography images, and use their expertise to develop data models that can better predict the presence of breast cancer.